Distinct Patterns Of Adherence Research Articles

Predictors of adherence to direct oral anticoagulants after cardiovascular or bleeding events in Medicare Advantage Plan enrollees with atrial fibrillation.

Direct oral anticoagulants (DOACs) are recommended for patients with atrial fibrillation (AF) given their improved safety profile. Suboptimal adherence to DOACs remains a significant concern among individuals with AF. However, the extent of adherence to DOACs following a cardiovascular or bleeding event has not been fully evaluated. To evaluate the pattern of adherence trajectories of DOACs after a cardiovascular or bleeding event and to investigate the sociodemographic and clinical predictors associated with each adherence trajectory by using claims-based data. This retrospective study was conducted among patients with AF prescribed with DOACs (dabigatran/apixaban/rivaroxaban) between July 2016 and December 2017 and who were continuously enrolled in the Texas-based Medicare Advantage Plan. Patients who experienced a cardiovascular or bleeding event while using the DOACs were further included in the analysis. The sample was limited to patients who experienced a clinical event such as a cardiovascular or bleeding event while using the DOACs. The clinical events considered in this study were cardiovascular (stroke, congestive heart failure, myocardial infarction, systemic embolism) and bleeding events. To assess adherence patterns, each patient with a DOAC prescription was followed up for a year after experiencing a clinical event. The monthly adherence to DOACs after these events was evaluated using the proportion of days covered (PDC). A group-based trajectory model incorporated the monthly PDC to classify groups of patients based on their distinct patterns of adherence. Predictors associated with each trajectory were assessed using a multinomial logistic regression model, with the adherent trajectory serving as the reference group in the outcome variable. Among the 694 patients with AF who experienced clinical events after the initiation of DOACs, 3 distinct adherence trajectories were identified: intermediate nonadherent (30.50%), adherent (37.7%), and low adherent (31.8%); the mean PDC was 0.47 for the intermediate nonadherent trajectory, 0.93 for the adherent trajectory, and 0.01 for low adherent trajectory. The low-income subsidy was significantly associated with lower adherence trajectories (odds ratio [OR] = 4.81; 95% CI = 3.07-7.51) and with intermediate nonadherent trajectories (OR = 1.57; 95% CI = 1.06-2.34). Also, nonsteroidal anti-inflammatory drug use was significantly associated with lower adherence trajectories (OR = 5.10; 95% CI = 1.95-13.36) and intermediate nonadherent trajectories (OR = 3.17; 95% CI = 1.26-7.93). Other predictors significantly associated with both nonadherent trajectories are type of DOACs (OR = 0.53; 95% CI = 0.35-0.79), presence of coronary artery disease (OR = 1.89; 95% CI = 1.01-3.55), and having 2 or more clinical events (OR = 1.65; 95% CI = 1.09-2.50). Predictors identified provide valuable insights into the suboptimal adherence of DOACs among Medicare Advantage Plan enrollees with AF, which can guide the development of targeted interventions to enhance adherence in this high-risk patient population.

Abstract 15326: A Wearable Accelerometry and AI Framework for Phenotyping Step-Count Trajectories in Older Adults Undergoing Cardiac Rehabilitation

Background: Wearable accelerometers, which record daily physical activity (PA) patterns over time, are a common component of mobile health-enabled cardiac rehabilitation (mHealth-CR). Artificial intelligence (AI) algorithms may be able to identify personalized insights of PA patterns to optimize mHealth-CR adherence. Methods: We used data from the RESILIENT trial, an ongoing randomized clinical trial of mHealth-CR in patients aged ≥65 years with ischemic heart disease. All participants in the intervention arm received Fitbit devices for activity tracking. We analyzed Fitbit step-count data collected over 3 months in these individuals using an AI framework based on functional principal component analysis (FPCA) and k-means clustering to identify distinct step count-based PA phenotypes. We then also computed adherence to using the Fitbit over time and clustered resultant adherence trajectories to identify distinct adherence patterns within the participant cohort. Results: We analyzed data from the first 75 intervention arm participants in RESILIENT (mean age 72 years, 19% female, 32% non-White). Using our AI framework, we identified four PA phenotypes based on their step count trends over 3 months: Unchanged (n=32), Steady Increase (n=14), Increase then Decrease (n=13), and Decrease and Increase (n=14) ( Figure 1A ). Two participants’ step count trajectories were not classifiable within this framework. We also examined changes in adherence to Fitbit use over 3 months based on a minimum step count cutoff for nonadherence (<128 steps). Applying our AI framework, we identified four adherence phenotypes: Constantly adherent (n=39) , Mostly adherent (n=13) , Moderately adherent (n=14) , and Mildly adherent (n=9) (( Figure 1B )). Conclusions: Wearable accelerometry combined with AI can provide novel insights into PA patterns during CR, which may help inform personalized PA regimens that can maximize adherence to mHealth-CR.

Trajectories of adherence to extended treatment with direct oral anticoagulants and risks of recurrent venous thromboembolism and major bleeding.

BACKGROUND: Little is known about medication adherence patterns and their association with effectiveness and safety among patients with venous thromboembolism (VTE) receiving direct oral anticoagulant (DOAC) therapy beyond 3-6 months of initial treatment. OBJECTIVE: To examine the associations between adherence trajectories of extended treatment with DOAC and the risks of recurrent VTE and major bleeding among patients with VTE. METHODS: We conducted a retrospective cohort study of patients with incident VTE who completed 6 months of initial anticoagulant treatment and received either DOAC extended therapy or no extended therapy using MarketScan Commercial and Medicare Supplemental databases (2013-2019). We used group-based trajectory models to identify distinct adherence patterns during extended treatment. Using inverse probability treatment weighted Cox proportional hazards models, we examined the association between the adherence trajectories and the risks of recurrent VTE and major bleeding. RESULTS: Among 10,960 patients with extended treatment with DOACs (rivaroxaban, apixaban, dabigatran, edoxaban) and 5,133 patients with no extended treatment, we identified 4 distinct trajectories (consistently high, gradually declining, rapidly declining, and no extended treatment). Compared with the no extended treatment group, the groups with consistently high adherence (hazard ratio = 0.09, 95% CI = 0.05-0.17) and with gradually declining adherence (0.13, 0.03-0.53) showed decreased recurrent VTE risk without increased major bleeding risk (consistently high adherence 1.19, 0.71-1.99; gradually declining adherence 1.96, 0.81-4.70). There was no difference in the risk of recurrent VTE (0.34, 0.10-1.16) for the group with rapidly declining adherence, but this group was associated with increased major bleeding risk (2.65, 1.01-6.92). CONCLUSIONS: Our findings underscore the clinical importance of continuing and remaining adherent to extended DOAC treatment without increased major bleeding risk for patients with VTE. DISCLOSURES: This research was supported by the BMS/Pfizer Alliance American Thrombosis Investigator Initiated Research Program. The funding source had no role in the design, collection, analysis, or interpretation of the data or the decision to submit the article for publication. Dr Lo-Ciganic reported receiving research funding from Merck Sharp & Dohme Corp. Dr Dietrich reported receiving honorarium for training and education from BMS/Pfizer. Dr DeRemer is a stockholder of Portola Pharmaceuticals and reported receiving personal fees for advisory board meeting from BMS. No other disclosures were reported.

Adherence to daily, oral TDF/FTC PrEP during periconception among HIV-exposed South African women.

Daily, oral tenofovir disoproxil fumarate/emtricitabine (TDF/FTC) as pre-exposure prophylaxis (PrEP) reduces HIV acquisition for African women. Adherence is key to efficacy and patterns of adherence can be highly variable in real-world settings. Using group-based trajectory modeling (GBTM), we sought to identify distinct patterns of periconception PrEP adherence and evaluate potential baseline predictors of such adherence trajectories. We conducted a single-arm longitudinal study for women aged 18-35 years living in Durban, South Africa with personal or partner plans for pregnancy with a partner with HIV or of unknown serostatus. Participants were offered safer conception counseling, including daily oral PrEP; women who initiated PrEP were given a bottle with an electronic pillcap that recorded when device opens. Weekly adherence to daily PrEP was modeled using GBTM with a censored normal outcome distribution as a function of weeks since PrEP initiation. The number and functional form of the adherence trajectory groups were primarily selected based on Bayesian information criteria (BIC) and confirmed by mean estimated probabilities of group membership. A multivariable version of the selected model assessed baseline predictors of membership in adherence trajectory groups. Overall mean (95% CI) adherence to PrEP was 63% (60%, 67%). We identified four groups of women with distinct patterns of adherence: (1) high (i.e., ≥6 doses per week) steady adherence throughout follow-up (22% of PrEP initiators); (2) moderate (i.e., 4-5 doses per week), but steady adherence (31%); (3) initially high, but consistently declining adherence (21%); and (4) initially moderate adherence, followed by a rapid decline and subsequent rebound (26%). In multivariable-adjusted analyses, older age was associated with membership in the high, steady adherence group as compared to the group identified with an adherence trajectory of initially high, then decline, and finally a rebound. GBTM is useful for exploring potential heterogeneity in longitudinal patterns of medication adherence. Although a large proportion of women in this study achieved high levels of adherence by electronic pillcap initially, far fewer women maintained these levels consistently. Knowledge of different adherence trajectories could be used to develop targeted strategies for optimizing HIV prevention during periconception.

Patterns of statin adherence in primary cardiovascular disease prevention during the pandemic.

Background: Study of medication adherence patterns can help identify patients who would benefit from effective interventions to improve adherence. Objectives: To identify and compare groups of statin users based on their adherence patterns before and during the COVID-19 pandemic, to characterize the profile of users in each group, and to analyze predictors of distinct adherence patterns. Methods: Participants of the CARhES (CArdiovascular Risk factors for HEalth Services research) cohort, comprising individuals aged >16years, residing in Aragón (Spain), with hypertension, diabetes mellitus and/or dyslipidemia, took part in this observational longitudinal study. Individuals who began statin therapy during January-June 2019 were selected and followed up until June 2021. Those with a cardiovascular event before or during follow-up were excluded. Data were obtained from healthcare system data sources. Statin treatment adherence during the implementation phase was estimated bimonthly using the Continuous Medication Availability (CMA9) function in the AdhereR package. Group-based trajectory models were developed to group statin users according to their adherence pattern during July 2019-June 2021. Group characteristics were compared and predictors of each adherence pattern were analyzed using multinomial logistic regression. Results: Of 15,332 new statin users, 30.8% had a mean CMA9 ≥80% for the entire study period. Four distinct adherence patterns were identified: high adherence (37.2% of the study population); poor adherence (35.6%); occasional use (14.9%); and gradual decline (12.3%). The latter two groups included users who showed a change in adherence (increase or decrease) during the pandemic emergence. Users with suboptimal adherence were likely to be younger, not pensioners, not institutionalized, with low morbidity burden and a low number of comorbidities. Female sex and switching between statins of different intensity increased the likelihood of belonging to the occasional use group, in which improved adherence coincided with the pandemic. Conclusion: We identified four distinct adherence patterns in a population of new statin users; two of them modified their adherence during the pandemic. Characterization of these groups could enable more effective distribution of resources in future similar crisis and the routine implementation of patient-centered interventions to improve medication adherence.

Group based trajectory modeling to assess adherence to oral anticoagulants among atrial fibrillation patients with comorbidities: a retrospective study.

Poor adherence to oral anticoagulants is a significant problem in atrial fibrillation (AF) patients with comorbidities as it increases the risk for cardiac and thromboembolic events. The primary objective was to evaluate adherence to direct oral anticoagulants (DOACs) or warfarin using group-based trajectory modeling (GBTM). The secondary objective was to identify the predictors of adherence to oral anticoagulants. Finally, to report the drug interactions with DOACs/warfarin. This retrospective study was conducted among continuously enrolled Medicare Advantage Plan members from January 2016-December 2019. AF patients with comorbid hypertension, diabetes and hyperlipidemia using warfarin/DOACs were included. Monthly adherence to DOAC/warfarin was measured using proportion of days covered (PDC) and then modeled in a logistic GBTM to identify the distinct patterns of adherence. Logistic regression model was conducted to identify the predictors of adherence to oral anticoagulants adjusting for all baseline characteristics. Concomitant use of DOACs/warfarin with CYP3A4,P-gp inhibitors were measured. Among 317 patients, 137 (43.2%) and 79 (24.9%) were DOAC, and warfarin users, respectively. The adherence trajectory model for DOACs included gradual decline (40.4%), adherent (38.8%), and rapid decline (20.8%). The adherence trajectories for warfarin adherence included gradual decline (8.9%), adherent (59.4%), and gaps in adherence (21.7%). Predictors of adherence included type of oral anticoagulant, stroke risk score, low-income subsidy, and baseline PDC. CYP3A4,P-gp drugs were co-administered with DOACs /warfarin resulting in adverse events. Adherence to oral anticoagulants is suboptimal. Interventions tailored according to past adherence trajectories may be effective in improving patient's adherence.

Novel Methods of Measuring Adherence Patterns Reveal Adherence Phenotypes with Distinct Asthma Outcomes.

Rationale: Poor adherence with asthma controller medication contributes to worse symptom control and increased exacerbation risk. Adherence is often expressed as the mean proportion of prescribed doses taken over a period of 6-12 months. New metrics may capture individual day-to-day variability patterns linked with distinct clinical outcomes. Objectives: To test the hypotheses that novel time- and dose-based adherence variability metrics offer independent value to mean adherence in identifying distinct adherence patterns that are associated with symptom control (Asthma Control Test [ACT] score) and exacerbation risk, using electronically recorded medication data from a 6-month cluster randomized trial examining the effect of inhaler reminders on adherence. Methods: Adherence metrics were calculated from the first 2 months (Months 0-2) of the study period. In addition to mean adherence (percentage prescribed puffs/day taken), we examined novel metrics, including time adherence area under the curve (T-AUC), reflecting cumulative gaps in adherence over time; entropy, reflecting disorder in the ways in which a patient changed their medication dose adherence from day to day; and standard deviation of the percentage prescribed puffs/day taken. Dominant metrics identified from factor analysis were included in hierarchical clustering analysis. We compared the resultant clusters in terms of outcomes over Months 2-6 and exacerbation risk over the entire study period. Results: Two factors explained >65% of the total variance in adherence, primarily driven by T-AUC and entropy. Two main patient clusters based on their adherence metrics were identified: cluster 1 (high time adherence, n = 75) had better T-AUC (i.e., fewer gaps between medication-taking days) than cluster 2 (low time adherence, n = 23). Though both clusters had similar symptom control at 2 months, cluster 1 showed less subsequent decline in ACT over Months 2-6 (median [interquartile range] change in ACT score: 1 [-1 to 4] vs. -2 [-3.75 to 0.75]; P = 0.012), and had better symptom control at 6 months (ACT score: 20 [17-23] vs. 17 [15-20]; P = 0.034). There were no significant differences between the clusters in terms of proportion of exacerbators or time to exacerbation. Conclusions: Novel metrics showed that low time adherence was associated with greater risk of decline in asthma symptom control. Adherence patterns may exhibit "memory" relevant to future clinical status, warranting validation in a larger dataset.

Using group-based trajectory modeling to characterize the association of past ACEIs/ARBs adherence with subsequent statin adherence patterns among new statin users

BackgroundDespite well-documented benefits, statin adherence remains suboptimal. Studies have suggested that previous adherence to other chronic medications is a strong predictor of future adherence to newly initiated statins. Group-based trajectory modeling (GBTM) has been applied as a method to longitudinally depict the dynamic nature of adherence. ObjectivesThis study aimed to examine the association between patients’ adherence patterns to newly initiated statins and previous adherence trajectories of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs) using GBTM. MethodsThis retrospective cohort study was conducted among continuously enrolled statin initiators using claims data. Patients were included if they had ACEI/ARB use within 1 year before statin initiation (preindex period). Monthly adherence to ACEIs/ARBs was calculated during the preindex period and monthly adherence to statins was assessed 1 year after statin initiation using proportion of days covered (PDC). The monthly PDCs were modeled as a longitudinal response in a logistic GBTM to provide distinct patterns of adherence for ACEIs/ARBs and statins, separately. A multinomial logistic regression was conducted to determine an association between ACEI/ARB adherence trajectories and future statin trajectories, controlling for patient characteristics. ResultsA total of 1078 patients were categorized into 4 distinct statin adherence trajectories: adherent (40.8%), gradual decline (37.4%), gaps in adherence (13.9%), and rapid decline (7.9%). Patients were further categorized into 4 groups on the basis of their distinct past ACEIs/ARBs trajectories: adherent (43%), gaps in adherence (29%), delayed nonadherence (15.2%), and gradual decline (12.8%). In the multinomial logistic regression, patients in the gaps in adherence or gradual decline groups were more likely to follow similar trajectories for future statin use than the adherent trajectory. ConclusionPrevious adherence trajectories of ACEIs/ARBs may predict future adherence patterns for newly initiated statins. Knowledge of past medication-taking behavior could provide valuable information for developing tailored interventions to improve adherence.

PDB38 Using Group-Based Trajectory Modeling (GBTM) to Characterize the Association of Past ACEI/ARBS Adherence Trajectories with Subsequent Statin Adherence Trajectories Among New Statin Users

Statins are among the most widely prescribed medications in the US. Despite well-documented benefits, statin adherence remains suboptimal. This study aims to examine the association between adherence patterns to newly initiated statins and previous adherence patterns of angiotensin-converting enzyme inhibitors (ACEI)/angiotensin receptor blockers (ARB) using GBTM. This retrospective cohort study was conducted among statin initiators using administrative claims data. Patients were included if they had continuous enrollment and at least two prescriptions for ACEI/ARBs 1 year prior to statin initiation (index date). GBTM was used to characterize adherence patterns for statins during the 12 months following the index date as well as for the ACEI/ARBs during the 12 months before the index date. Monthly adherence was calculated using proportion of days covered (PDC), which were modeled as a longitudinal response in a logistic group-based trajectory model to provide distinct adherence patterns. A multinomial logistic regression was conducted to examine the association between baseline ACEI/ARB adherence trajectories with the outcome of statin trajectories (adherent trajectory as the reference group), controlling for demographic and clinical characteristics. Other predictors of statin trajectories were also evaluated. 1,078 patients were included in the analysis and categorized into 4 distinct statin adherence trajectories: adherent (40.8%); gradual decline (37.4%); gaps in adherence (13.9%), and rapid decline (7.9%). Patients were further categorized into 4 groups based on their past ACEI/ARBs trajectories: adherent (43%); gaps in adherence (29%); late nonadherence (15.2%), and gradual decline (12.8%). In the regression model, patients in the gaps in adherence or gradual decline groups were significantly more likely to follow similar trajectories for statin use as compared to the adherent trajectory. Previous adherence trajectories of ACEI/ARBs may predict future adherence patterns for newly initiated statins. Knowledge of past medication-taking behavior could provide valuable information to guide the development of tailored interventions to improve adherence.

Boldness Personality Traits Are Associated With Reduced Risk Perceptions and Adoption of Protective Behaviors During the First COVID-19 Outbreak.

The containment measures imposed during the first COVID-19 outbreak required economic, social, and behavioral changes to minimize the spread of the coronavirus. Some studies have focused on how personality predicts distinct patterns of adherence to protective measures with psychopathic and antisocial traits predicting reduced engagement in such measures. In this study we extended previous findings by analyzing how boldness, meanness, and disinhibition psychopathic traits relate with both risk perceptions and protective behaviors during the first COVID-19 outbreak. A sample of 194 individuals (24% male) engaged in the survey, were assessed for psychopathic traits with the Triarchic Psychopathy Measure, and completed a COVID-19 survey targeting risk perceptions (spread, risk of becoming infected, state anxiety toward the COVID-19, and perceived risk of specific behaviors) and frequency of protective behaviors (e.g., not engaging in social distancing). Overall results show that boldness predicts reduced estimate of COVID-19 spread, reduced perceived risk of becoming infected, reduced state anxiety toward COVID-19, and reduced frequency of protective behaviors. Exploratory mediation models suggest that risk perceptions are not significant mediators of the association between psychopathic traits and reduced engagement in protective behaviors. Our results unveil that psychopathic traits affect risk perceptions and the propensity to engage in protective measures, emphasizing the need to accommodate these personality features in the public health strategy to control the COVID-19 spread.

Distinct Patterns of Host Adherence by Neisseria gonorrhoeae Isolated from Experimental Gonorrhea.

Neisseria gonorrhoeae (N. gonorrhoeae, gonococci, or GC), the etiologic agent of gonorrhea, is a human-obligate bacterial pathogen. The GC surface contains pili that mediate the adherence to host cells. Studies have shown that GC pili, coded by pilin genes, undergo remarkable changes during human experimental gonorrhea, possibly generated by DNA phase variation during infection. The question that arises is whether the changes in pilins can alter the adherence capacity of N. gonorrhoeae to host cells. In this study, six variants initially isolated from male volunteers infected with one single clone of GC were examined for their adherence patterns with human Chang conjunctiva cells. In this study, we showed that the variants showed distinct adherence patterns to this cell line under light microscopy and scanning electron microscopy. Moreover, two reisolates showed higher adherence capacities than that of the input strain. The results provide an additional example as to how the pilus variation may play a role in the pathogenesis of N. gonorrhoeae.

<p>Group-Based Trajectory Modeling to Identify Patterns of Adherence and Its Predictors Among Older Adults on Angiotensin-Converting Enzyme Inhibitors (ACEIs)/Angiotensin Receptor Blockers (ARBs)</p>

PurposeCommonly prescribed medications among patients with comorbid diabetes mellitus and hypertension include ARBs and ACEIs. However, these medications are associated with suboptimal adherence leading to inadequately controlled blood pressure. Unlike traditional single estimates of proportion of days covered (PDC), group-based trajectory modeling (GBTM) can graphically display the dynamic nature of adherence. The objective of this study was to evaluate adherence using GBTMs among patients prescribed ACEI/ARBs and identify predictors associated with each adherence trajectory.Patients and MethodsPatients with an ACEI/ARBs prescription were identified between July 2017 and December 2017 using a Medicare Advantage dataset. PDC was used to measure monthly patient adherence during the one-year follow-up period. The monthly PDC was added to a logistic group-based trajectory model to provide distinct patterns of adherence. Further, a multinomial logistic regression was conducted to determine predictors of each identified adherence trajectory. Predictors included various socio-demographic and clinical patient characteristics.ResultsA total of 22,774 patients were included in the analysis and categorized into 4 distinct adherence trajectories: rapid decline (12.6%); adherent (58.5%); gaps in adherence (12.2%), and gradual decline (16.6%). Significant predictors associated with all lower adherence trajectories included 90 days refill, >2 number of other medications, ≥1 hospitalizations, and prevalent users. Significant predictors associated with the rapid decline trajectory included male sex, comorbidities, and increased CMS risk score. Further, significant predictors associated with the gaps in adherence trajectory included increasing age, and comorbidities. Lastly, significant predictors associated with the gradual decline trajectory included increasing age, no health plan subsidy, comorbidities, and increasing CMS risk score.ConclusionIdentifying various patient characteristics associated with non-adherent trajectories can guide the development of tailored interventions to enhance adherence to ACEI/ARBs.

Enteropathogenic Escherichia coli Infection in Cancer and Immunosuppressed Patients.

The role of enteropathogenic Escherichia coli (EPEC) as a cause of diarrhea in cancer and immunocompromised patients is controversial. Quantitation of fecal bacterial loads has been proposed as a method to differentiate colonized from truly infected patients. We studied 77 adult cancer and immunosuppressed patients with diarrhea and EPEC identified in stools by FilmArray, 25 patients with pathogen-negative diarrhea, and 21 healthy adults without diarrhea. Stools were studied by quantitative polymerase chain reaction (qRT-PCR) for EPEC genes eaeA and lifA/efa-1 and strains characterized for virulence factors and adherence to human intestinal enteroids (HIEs). Patients with EPEC were more likely to have community-acquired diarrhea (odds ratio, 3.82 [95% confidence interval, 1.5-10.0]; P = .008) compared with pathogen-negative cases. Although EPEC was identified in 3 of 21 (14%) healthy subjects by qPCR, the bacterial burden was low compared to patients with diarrhea (≤55 vs median, 6 × 104 bacteria/mg stool; P < .001). Among EPEC patients, the bacterial burden was higher in those who were immunosuppressed (median, 6.7 × 103 vs 55 bacteria/mg; P < .001) and those with fecal lifA/ifa-1 (median, 5 × 104 vs 120 bacteria/mg; P = .015). Response to antimicrobial therapy was seen in 44 of 48 (92%) patients with EPEC as the sole pathogen. Antimicrobial resistance was common and strains exhibited distinct patterns of adherence with variable cytotoxicity when studied in HIEs. Cancer care was delayed in 13% of patients. Immunosuppressed cancer patients with EPEC-associated diarrhea carry high burden of EPEC with strains that are resistant to antibiotics, exhibit novel patterns of adherence when studied in HIEs, and interfere with cancer care.

Data driven decision making to characterize clinical personas of parents of children with cystic fibrosis: a mixed methods study

BackgroundBeginning at a young age, children with cystic fibrosis (CF) embark on demanding care regimens that pose challenges to parents. We examined the extent to which clinical, demographic and psychosocial features inform patterns of adherence to pulmonary therapies and how these patterns can be used to develop clinical personas, defined as aspects of adherence barriers that are presented by parents and/or perceived by clinicians, in order to enhance personalized CF care delivery.MethodsWe undertook an explanatory sequential mixed-methods study consisting of i) multivariate clustering to create clusters corresponding to parental adherence patterns (quantitative phase); ii) parental participant interviews to create clinical personas interpreted from clustering (qualitative phase). Clinical, demographic and psychosocial features were used in supervised clustering against clinical endpoints, which included adherence to airway clearance and aerosolized medications and self-efficacy score, which was used as a feature for modeling adherence. Clinical implications were developed for each persona by combing quantitative and qualitative data (integration phase).ResultsThe quantitative phase showed that the 87 parent participants were segmented into three distinct patterns of adherence based on use of aerosolized medication and practice of airway clearance. Patterns were primarily influenced by self-efficacy, distance to CF care center and child BMI percentile. The two key patterns that emerged for the self-efficacy model were most heavily influenced by distance to CF care center and child BMI percentile. Eight clinical personas were developed in the qualitative phase from parent and clinician participant feedback of latent components from these models. Findings from the integration phase include recommendations to overcome specific challenges with maintaining treatment regimens and increasing support from social networks.ConclusionsAdherence patterns from multivariate models and resulting parent personas with their corresponding clinical implications have utility as clinical decision support tools and capabilities for tailoring intervention study designs that promote adherence.

Adherence to Antiretroviral Regimens in Australia: A Nationwide Cohort Study.

The lifetime use of combination antiretroviral therapy (cART) highlights the need to understand patterns of and factors associated with adherence to cART. In this cohort study using a 10% random sample of dispensing claims data for eligible Australians, we identified 2042 people dispensed cART between January 2016 and December 2017 (mean age 48.0 ± 12.0 years old, 88.6% male, and 85.9% treatment experienced). We considered people to be adherent if the proportion of treatment coverage days was ≥80% in the 360 days after their first observed cART dispensing. We also used group-based trajectory modeling (GBTM) to examine different patterns of adherence for 360 days from first observed cART dispensing. Most commonly, people receiving cART were treated with two nucleoside/nucleotide reverse transcriptase inhibitors with an integrase strand transfer inhibitors (INSTI-46.6%). Overall, 1708 people [83.6% (95% confidential interval 82.0-85.3%)] remained adherent over 360 days. GBTM identified three distinct adherence patterns: nearly always adherent [67.8% (63.7-71.9%) of the cohort], moderate adherence [26.6% (23.0-30.1%)], and low adherence [5.6% (4.1-7.2%)]. People were more likely to belong to the "nearly always adherent" trajectory if they were older (per additional year of age), treated with an INSTI regimen, and on treatment for more than 6 months. Our study demonstrates that the 360-day adherence to cART is generally high, but approximately one-third maintain a moderate or low adherence pattern. The use of INSTI regimens and additional support of treatment adherence, especially among younger people and those initiating therapy, may further improve adherence.

Enhancing Statin Adherence Using a Motivational Interviewing Intervention and Past Adherence Trajectories in Patients with Suboptimal Adherence.

Statins have been shown to be effective in reducing the occurrence of cardiovascular (CV) events and are widely prescribed for the risk reduction of CV diseases and recurrent CV events. However, poor adherence prevents some patients from receiving the maximum benefit of the therapy. Motivational interviewing (MoI) is a patient-centered collaborative approach that can be used to improve medication adherence. Group-based trajectory modeling depicts patterns of adherence over time and may help tailor the MoI intervention to further enhance adherence. To assess the effect of a phone-based MoI intervention tailored by patients' past adherence trajectory in improving adherence to statins among patients in a Medicare Advantage prescription drug plan (MAPD). Patients continuously enrolled in an MAPD from 2013 to 2017 with a statin prescription between January and June 2015 to allow 2 years of pre-index period and 1 year of follow-up were included in the study. Adherence to statins was measured monthly during the 1-year follow-up as proportion of days covered (PDC) and incorporated into a group-based trajectory model to provide 4 distinct patterns of adherence: adherent, rapid decline, gradual decline, and gaps in adherence. Patients in the 3 nonadherent groups were randomized to either control or intervention. The intervention was an initial counseling call and up to 2 monthly follow-up calls by pharmacy students trained in MoI, providing education consistent with a previously identified pattern of use. Refill data at 6 months post-intervention were evaluated to examine the intervention's effect on PDC, as continuous and dichotomized as PDC ≥ 0.8, as well as discontinuation. Multivariable regression adjusted for baseline demographics, clinical characteristics, and past adherence trajectory. There were 152 patients included in the analysis who received MoI phone calls and 304 randomly selected controls. Mean PDC for the intervention group (0.67 ± 0.3) was significantly higher than the control (0.55 ± 0.4; P < 0.001). The intervention group was also less likely to discontinue (OR = 0.38; 95% CI = 0.19-0.76) and more likely to be adherent in the linear regression model (β = 12.4; P < 0.001) as well as in the logistic regression model (OR = 1.87; 95% CI = 1.18-2.95). Previous adherence trajectories were significantly associated with adherence in the follow-up. Patients who received the MoI intervention were more likely to be adherent and less likely to discontinue the statin in the 6 months follow-up compared with controls. Future research can identify other approaches to tailor interventions and expand the intervention to other languages. This intervention may also prove valuable to improve adherence to other medications for chronic and asymptomatic diseases. This study was funded by Regeneron Pharmaceuticals, which provided critical input during study design, implementation, and manuscript preparation. Abughosh reports grants from Sanofi, BMS/Pfizer, and Valeant Pharmaceuticals, unrelated to this study. Vadhariya reports a past internship at Regeneron Pharmaceuticals, unrelated to this study. Esse, Serna, and Gallardo are employees of CareAllies, a Cigna subsidiary. Boklage is an employee of Regeneron Pharmaceuticals. Choi was an employee of Sanofi during this study. Johnson, Essien, Fleming, and Holstad have nothing to disclose. A poster based on this study was presented at AMCP Nexus 2018; October 22-25, 2018; Orlando, FL.

Patterns of Oral PrEP Adherence and HIV Risk Among Eastern African Women in HIV Serodiscordant Partnerships.

Understanding how women use PrEP is important for developing successful implementation programs. We hypothesized there are distinct patterns of adherence, related to HIV risk and other factors. We identified patterns of PrEP adherence and HIV risk behavior over the first 6months of PrEP use, using data from 233 HIV-uninfected women in high-risk serodiscordant couples in a demonstration project in Kenya & Uganda. We modeled PrEP adherence, assessed by daily electronic monitoring, and HIV risk behavior using group-based trajectory models. We tested baseline covariates and risk behavior group as predictors of adherence patterns. There were four distinct adherence patterns: high steady adherence (55% of population), moderate steady (29%), late declining (8%), and early declining (9%). No baseline characteristics significantly differed between adherence patterns. Adherence patterns differed in average weekly doses (6.7 vs 5.4 vs 4.1 vs 1.5, respectively). Two risk behavior groups were identified: steady HIV risk (78% of population) and declining (22%). Compared to women with declining HIV risk behavior, women with steady risk behavior were more likely to have high steady adherence (61% vs 35%) and less likely to have early (6% vs 17%) or late (4% vs 19%) declining adherence. Women's use of PrEP was associated with concurrent HIV risk behavior; higher risk was associated with higher, sustained adherence.

Patterns of Adherence to Oral Atypical Antipsychotics Among Patients Diagnosed with Schizophrenia.

Poor medication adherence contributes to negative treatment response, symptom relapse, and hospitalizations in schizophrenia. Many health plans use claims-based measures like medication possession ratios or proportion of days covered (PDC) to measure patient adherence to antipsychotics. Classifying patients solely on the basis of a single average PDC measure, however, may mask clinically meaningful variations over time in how patients arrive at an average PDC level. To model patterns of medication adherence evolving over time for patients with schizophrenia who initiated treatment with an oral atypical antipsychotic and, based on these patterns, to identify groups of patients with different adherence behaviors. We analyzed health insurance claims for patients aged ≥ 18 years with schizophrenia and newly prescribed oral atypical antipsychotics in 2007-2013 from 3 U.S. insurance claims databases: Truven MarketScan (Medicaid and commercial) and Humana (Medicare). Group-based trajectory modeling (GBTM) was used to stratify patients into groups with distinct trends in adherence and to estimate trends for each group. The response variable was the probability of adherence (defined as PDC ≥ 80%) in each 30-day period after the patient initiated antipsychotic therapy. GBTM proceeds from the premise that there are multiple distinct adherence groups. Patient demographics, health status characteristics, and health care resource use metrics were used to identify differences in patient populations across adherence trajectory groups. Among the 29,607 patients who met the inclusion criteria, 6 distinct adherence trajectory groups emerged from the data: adherent (33%); gradual discontinuation after 3 months (15%), 6 months (7%), and 9 months (5%); stop-start after 6 months (15%); and immediate discontinuation (25%). Compared to patients 18-24 years of age in the adherent group, patients displaying a stop-start pattern after 6 months had greater odds of having a history of drug abuse (OR = 1.46; 95% CI = 1.26-1.66; P < 0.001), alcohol abuse (OR = 1.34; 95% CI = 1.14-1.53; P< 0.001), and a codiagnosis of major depressive disorder (OR = 1.24; 95% CI = 1.05-1.44; P < 0.001) and were less likely to be aged 35-54 years (OR = 0.66; 95% CI = 0.46-0.85; P < 0.001). Longitudinal medication adherence patterns can be expressed as distinct trajectories associated with specific patient characteristics and health care utilization patterns. We found 6 distinct patterns of adherence to antipsychotics over 12 months. Patients in different groups may warrant different types of clinical interventions to prevent hospitalizations, longer hospital stays, and increased clinical complexity. For example, clinicians may consider regular home visits, assertive community treatment, and other related interventions for patients at high risk of immediate discontinuation. Health plans should consider supplementing claims-based adherence measures with new technologies that are able to track patient adherence patterns over time. Otsuka Pharmaceutical Development & Commercialization provided support for this research. MacEwan and Shafrin are employees of Precision Health Economics, which was contracted by Otsuka Pharmaceutical Development & Commercialization to conduct this study. Lakdawalla is the Chief Scientific Officer and a founding partner of Precision Health Economics. Forma is an employee of Otsuka Pharmaceutical Development & Commercialization. Hatch is a former employee of Otsuka Pharmaceutical Development & Commercialization and is a current employee of ODH, Inc. Lindenmayer has received grant/research support from Janssen, Lilly, AstraZeneca, Johnson & Johnson, Pfizer, BMS, Otsuka, Dainippon, and Roche and is a consultant for Janssen, Lilly, Merck, Shire, and Lundbeck. Portions of this study were presented as a poster at the American Society of Clinical Psychopharmacology Annual Meeting in Miami Beach, Florida; June 23, 2015; and at the 28th Annual U.S. Psychiatric and Mental Health Congress; San Diego, California; September 12, 2015. Study concept and design were contributed by Forma, Ladkawalla, MacEwan, and Shafrin, along with Hatch and Lindenmayer. MacEwan, Shafrin, Forma, and Lakdawalla collected the data, along with Hatch and Lindenmayer. Data interpretation was performed by Hatch, Lindenmayer, MacEwan, and Shafrin, assisted by Forma and Lakdawalla. The manuscript was written and revised by MacEwan, Forma, and Shafrin, along with Hatch Lakdawalla, and Lindenmayer.

Predictors of Objectively Measured Medication Nonadherence in Adults With Heart Failure

Medication nonadherence rates are high. The factors predicting nonadherence in heart failure remain unclear. A sample of 202 adults with heart failure was enrolled from the northeastern United States and followed for 6 months. Specific aims were to describe the types of objectively measured medication adherence (eg, taking, timing, dosing, drug holidays) and to identify contributors to nonadherence 6 months after enrollment. Latent growth mixture modeling was used to identify distinct trajectories of adherence. Indicators of the 5 World Health Organization dimensions of adherence (socioeconomic, condition, therapy, patient, and healthcare system) were tested to identify contributors to nonadherence. Two distinct trajectories were identified and labeled persistent adherence (77.8%) and steep decline (22.3%). Three contributors to the steep decline in adherence were identified. Participants with lapses in attention (adjusted OR, 2.65; P=0.023), those with excessive daytime sleepiness (OR, 2.51; P=0.037), and those with ≥2 medication dosings per day (OR, 2.59; P=0.016) were more likely to have a steep decline in adherence over time than to have persistent adherence. Two distinct patterns of adherence were identified. Three potentially modifiable contributors to nonadherence have been identified.

In VitroAdherence Patterns ofShigellaSerogroups to Bovine Recto-Anal Junction Squamous Epithelial (RSE) Cells Are Similar to Those ofEscherichia coliO157

The aims of this study were to determine whether Shigella species, which are human gastrointestinal pathogens, can adhere to cattle recto-anal junction squamous epithelial (RSE) cells using a recently standardized in vitro adherence assay, and to compare their adherence patterns with that of Escherichia coli O157. Shigella dysenteriae (serogroup A), S. flexneri (serogroup B), S. boydii (serogroup C), and S. sonnei (serogroup D) were tested in adherence assays using both RSE and HEp-2 cells, in the presence or absence of D+mannose. Escherichia coli O157, which adheres to RSE cells in a Type I fimbriae-independent manner, was used as a positive control. Shigella serogroups A, B, D, but not C adhered to RSE cells with distinct adherence patterns in the presence of D+mannose. No such distinction could be made between the four Shigella serogroups based on the HEp-2 cell adherence patterns. Thus, this study provides evidence that certain Shigella serogroups adhere to RSE cells in a manner that is similar to the adherence pattern of E. coli O157. These unexpected observations of in vitro binding of these foodborne human pathogens to cells of the bovine gastrointestinal tract warrant evaluation of Shigella carriage by cattle using both experimental and observational studies, especially for serogroups B and D. Such studies are currently underway.